BSF: a parallel computation model for scalability estimation of iterative numerical algorithms on cluster computing systems

TL;DR

This paper introduces the BSF parallel computation model to estimate the scalability of iterative numerical algorithms on cluster systems, providing a predictive equation validated by large-scale experiments.

Contribution

The paper presents the BSF model and a new cost metric for predicting scalability boundaries of parallel algorithms on clusters.

Findings

The BSF model accurately predicts scalability limits.

Analytical estimations align with experimental results.

The model aids in designing scalable parallel algorithms.

Abstract

This paper examines a new parallel computation model called bulk synchronous farm (BSF) that focuses on estimating the scalability of compute-intensive iterative algorithms aimed at cluster computing systems. In the BSF model, a computer is a set of processor nodes connected by a network and organized according to the master/slave paradigm. A cost metric of the BSF model is presented. This cost metric requires the algorithm to be represented in the form of operations on lists. This allows us to derive an equation that predicts the scalability boundary of a parallel program: the maximum number of processor nodes after which the speedup begins to decrease. The paper includes several examples of applying the BSF model to designing and analyzing parallel nu-merical algorithms. The large-scale computational experiments conducted on a cluster computing system confirm the adequacy of the analytical estimations obtained using the BSF model.